Association of bone mineral density with lung function in a Chinese general population: the Xinxiang rural cohort study.

BACKGROUND: Bone mineral density (BMD) has been positively associated with lung function in patients diagnosed with respiratory diseases such as chronic obstructive pulmonary disease (COPD) and cystic fibrosis. However, the relationship between BMD and lung function is inconsistent in the general population. METHODS: To investigate the association between BMD and lung function in a Chinese general population, a total of 1024 adults aged 40-70 years old from Qiliying (an industrial polluted exposure area) and Langgongmiao (the reference area with non-industrial pollution) were recruited and underwent BMD and spirometry tests. RESULTS: Both BMD and lung function levels were lower in the exposed area compared to the reference area. In addition, BMD and lung function levels were also lower in females compared to males. Both Spearman and partial correlation analyses showed that BMD was positively correlated with FVC and FEV1. After adjusting linear regression analyses for potential confounding factors, every 0.1 g/cm2 drop in BMD was associated with 53.0 mL decrease in FVC and 33.5 mL decrease in FEV1. CONCLUSIONS: A reduction of BMD is associated with lower lung function in a general population from China.

An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China.

PURPOSE: Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR). METHODS: We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate . RESULTS: During 2004-2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0)12, NNNAR(7,1,4)12 and SARIMA(2,0,2)(1,1,0)12-NNNAR(3,1,7)12, respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results. CONCLUSION: The best-mimicking SARIMA-NNNAR hybrid model outperforms the best-simulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.

Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019.

Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of -5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.

Acute effects of air pollution on type II diabetes mellitus hospitalization in Shijiazhuang, China.

Air pollution has been considered as an important contributor to diabetes development. However, the evidence is fewer in developing countries where air pollution concentrations were much higher. In this study, we conduct a time-series study to investigate the acute adverse effect of six air pollutants on type II diabetes mellitus (T2DM) hospitalization in Shijiazhuang, China. An over-dispersed passion generalized addictive model adjusted for weather conditions, day of the week, and long-term and seasonal trends was used. Finally, a 10-µg/m3 increase of fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) corresponded to 0.53% (95% confidence interval = 0.22-0.83), 0.32% (95% CI = 0.10-0.55), 0.55% (95% CI = 0.04-1.07), 1.27% (95% CI = 0.33-2.22), and 0.04% (95% CI = 0.02-0.06) increment of T2DM hospitalization, respectively. The effects of PM2.5, PM10, and CO were robust when adjusted for co-pollutants. The associations appeared to be a little stronger in the cool season than in the warm season. And stronger associations were found in male and elderly (≥ 65 years) than in female and younger people (35-65 years). Our results contribute to the limited data in the scientific literature on acute effects of air pollution on type II diabetes mellitus in developing countries. MAIN FINDINGS: This is the first adverse effect evidence of air pollution on T2DM in Shijiazhuang, a severely polluted city in China. Males were more vulnerable than females in severe pollution.

Acute effects of ambient particulate matter pollution on hospital admissions for mental and behavioral disorders: A time-series study in Shijiazhuang, China.

Until now, few epidemiological studies have focused on the association between ambient particulate matter pollution and mental and behavioral disorders, especially in developing countries. Thus, a time-series study on the short-term association between both fine and inhalable particles (PM2.5 and PM10) and daily hospital admissions for mental and behavioral disorders in Shijiazhuang, China was conducted, from 2014 to 2016. An over-dispersed, generalized additive model was used to analyze the associations after controlling for time trend, weather conditions, day of the week, and holidays. In addition, the modification effects of age, sex, and season were estimated. A total of 9156 cases of hospital admissions for mental and behavioral disorders were identified. A 10 µg/m3 increase in a 3-day average concentration (lag02) of PM2.5 and PM10 correspond to an increase of 0.48% (95% confidence interval (CI): 0.18-0.79%) and 0.32% (95% CI: 0.03-0.62%) in daily hospital admission for mental and behavioral disorders, respectively. We found stronger associations of PM2.5 and PM10 with mental and behavioral disorders in male and elder individuals (≥45 years) than in female and younger individuals (<45 years). Further, results indicated a generally stronger association of PM2.5 with mental and behavioral disorders in the cool season than in the warm season. This research found a significant association between ambient PM2.5 and PM10 and hospital admission for mental and behavioral disorders in Shijiazhuang, China.